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Healthcare data mining: Predicting hospital length of stay of dengue patients
Wiratmadja I.I.a, Salamah S.Y.a, Govindaraju R.a
a Department of Industrial Engineering, Faculty of Industrial Technology, Institut Teknologi Bandung, Bandung, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018 Published by ITB Journal Publisher.Dengue is regarded as the most important mosquito-borne viral disease. Recently dengue has emerged as a public health burden in Southeast Asia and other tropical countries. At times when dengue re-emerges as an epidemic, hospitals are required to be able to handle patient flow fluctuation while maintaining their performance. This research applied a data mining technique to build a model that can predict in-patient hospital length of stay from the time of admission, which can be useful for effective decision-making that may lead to better clinical and resource management in hospitals. Using the C4.5 algorithm and a decision tree classifier, an accuracy of 71.57% and an area under the receiver operating characteristic (ROC) curve value of 0.761 were obtained. The decision tree showed that only 7 out of 21 input attributes affect the hospital length of stay prediction of dengue patients. The attribute with the highest impact was monocytes, followed by diastolic blood pressure, hematocrit, leucocytes, systolic blood pressure, comorbidity score, and lymphocytes. In this research also a prototype of a prediction system using the resulting model was developed.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Decision tree classifiers,Dengue,Diastolic blood pressures,Length of stay,Prediction systems,Receiver Operating Characteristic (ROC) curves,Resource management,Systolic blood pressure[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Data mining,Decision tree,Dengue,Hospital,Length of stay,Prediction[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.5614/j.eng.technol.sci.2018.50.1.8[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]